Real-world projects mean better prep.
What is it like to actually work as a Data science and Analystics in the field. Get an idea with projects straight from the industry.
Customer Churn Analysis Using Table
Conduct a customer churn analysis for a telecommunications company to identify factors leading to customer churn and provide actionable insights to help reduce churn rates.
Data Preparation & Cleaning
Use Advanced Excel to preprocess the telecom customer data, clean missing values, and organize key variables.
Statistical Analysis:
Apply statistical methods such as correlation analysis and hypothesis testing to determine key factors influencing customer churn.
Visualization & Insights
Use Tableau to create interactive dashboards that showcase customer churn risk, segmented customer groups, and patterns across various demographics and services.
Advanced Excel
Tableau
Superstore Dashboard Creation Using Power BI
Build a Superstore dashboard using Power BI, leveraging SQL and Excel for data preparation, validation, and analysis. The dashboard should provide insights into sales performance, profitability, customer segmentation, and regional trends.
Data Preparation:
Download the Superstore dataset, clean and validate the data using Advanced Excel techniques, ensuring data accuracy and consistency.
Database Management
Import the cleaned data into a SQL database and extract key metrics for analysis, such as sales trends, customer segments, and profitability.
Dashboard Development:
Use Power BI to connect to the SQL database, visualize the extracted data, and design a dashboard that highlights sales performance, profitability, customer segmentation, and regional insights.
Advanced Excel
SQL
Power BI
Heart Disease Prediction Using Machine Learning Algorithms
Develop a heart disease prediction system using machine learning algorithms and deploy the model using Flask/Streamlit for user interaction and real-time predictions.
Data Analysis & Preprocessing
Use Python to clean, explore, and prepare heart disease datasets for modeling.
Statistical Analysis
Perform statistical analysis to identify key factors influencing heart disease risk and ensure data validity.
Machine Learning Model Development<br Build and train machine learning models using algorithms like logistic regression, decision trees, or random forests for predicting heart disease.
Deployment:
Use Flask/Streamlit to deploy the machine learning model, enabling real-time predictions and insights on heart disease risk.
Python
